{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "140106c7-8ad4-496e-9c48-2a3fc2d46400",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "Copyright (c) MONAI Consortium  \n",
    "Licensed under the Apache License, Version 2.0 (the \"License\");  \n",
    "you may not use this file except in compliance with the License.  \n",
    "You may obtain a copy of the License at  \n",
    "&nbsp;&nbsp;&nbsp;&nbsp;http://www.apache.org/licenses/LICENSE-2.0  \n",
    "Unless required by applicable law or agreed to in writing, software  \n",
    "distributed under the License is distributed on an \"AS IS\" BASIS,  \n",
    "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  \n",
    "See the License for the specific language governing permissions and  \n",
    "limitations under the License.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6923e88c-10a0-4285-a797-3d3a6ed4c60c",
   "metadata": {},
   "source": [
    "# SPADE LDM\n",
    "\n",
    "In this tutorial, we apply SPADE normalisation [1] to a latent diffusion model, in a methodology initially published by Wang et al. [2] to perform semantic image synthesis. We apply to a subset of BraTS [3] that we registered to MNI space and resampled to 2mm isotropic space. Segmentations of cerebrospinal fluid, grey and white matter, deep grey matter and brainstem were obtained using Geodesic Information Flows (GIF) [4].\n",
    "\n",
    "[1] Park, T., Liu, M.-Y., Wang, T.-C., & Zhu, J.-Y. (2019). Semantic Image Synthesis with Spatially-Adaptive Normalization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.\n",
    "\n",
    "[2] Wang, W., Bao, J., Zhou, W., Chen, D., Chen, D., Yuan, L., & Li, H. (2022). Semantic Image Synthesis via Diffusion Models. https://doi.org/10.48550/arxiv.2207.00050\n",
    "\n",
    "[3] Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., Lanczi, L., Gerstner, E., Weber, M.-A., Arbel, T., Avants, B. B., Ayache, N., Buendia, P., Collins, D. L., Cordier, N., … van Leemput, K. (2015). The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Transactions on Medical Imaging, 34(10), 1993–2024. https://doi.org/10.1109/TMI.2014.2377694\n",
    "\n",
    "[4] Cardoso, M. J., Wolz, R., Modat, M., Fox, N. C., Rueckert, D., & Ourselin, S. (2012). Geodesic information flows. Medical Image Computing and Computer-Assisted Intervention : MICCAI ...  International Conference on Medical Image Computing and Computer-Assisted Intervention, 15(Pt 2), 262–270. https://doi.org/10.1007/978-3-642-33418-4_33"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3604c287a30a2b4",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "## Setup environment "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6732a08b84ec442",
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "!python -c \"import monai\" || pip install -q \"monai-weekly[tqdm]\"\n",
    "!python -c \"import matplotlib\" || pip install -q matplotlib\n",
    "!python -c \"import gdown\" || pip install -q gdown\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16e7adac407b6c34",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "## Setup imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1bb9ef03-4d0d-4c04-9bd9-e399e1b37b86",
   "metadata": {
    "is_executing": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import tempfile\n",
    "import zipfile\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from torch.amp import GradScaler, autocast\n",
    "import gdown\n",
    "import monai\n",
    "from monai.config import print_config\n",
    "from monai.utils import set_determinism\n",
    "from monai.data import DataLoader\n",
    "from monai.networks.nets import SPADEAutoencoderKL, SPADEDiffusionModelUNet, PatchDiscriminator\n",
    "from monai.networks.schedulers import DDPMScheduler\n",
    "from monai.losses import PatchAdversarialLoss, PerceptualLoss\n",
    "from monai.inferers import LatentDiffusionInferer\n",
    "\n",
    "print_config()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a98c6cf-b38e-4cba-8fb3-e07f67d0ec71",
   "metadata": {},
   "source": [
    "## Setup a data directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ec734e87-1415-40a7-98a6-a53ddf6350d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Temporary directory used: /tmp/tmpjboo3kuj \n"
     ]
    }
   ],
   "source": [
    "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
    "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
    "root_dir = Path(root_dir)\n",
    "print(\"Temporary directory used: %s \" % root_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e68de24df72cd5d",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "## Set deterministic training for reproducibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9b0659759c18728f",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "set_determinism(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11e6cd5c-7bf9-47ce-93dc-c7760f1bae9e",
   "metadata": {},
   "source": [
    "## Description of the data and download dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00d8b3be-8e21-4d8b-898e-2784c0d7640c",
   "metadata": {},
   "source": [
    "The data for this notebook comes from the brain tumour segmentation challenge (BraTS) [2]. The images have been registered to MNI space using ANTsPy, and then subsampled to 2mm isotropic resolution. Geodesic Information Flows (GIF) [3] has been used to segment 5 regions: cerebrospinal fluid (CSF), grey matter (GM), white matter (WM), deep grey matter (DGM) and brainstem. Tumour sub-layers were delineated manually as part of the challenge source data. \n",
    "In this subset, we have only used one MRI modality , T1. \n",
    "Segmentations in this subset are merged and consist of the following labels: 0 (background), 1 (CSF), 2 (GM), 3 (WM), 4 (DGM), 5 (brainstem), 6 (brainstem), 7 (non GD-enahncing tumour core), 8 (peritumoural oedema), 9 (GD-enhancing tumour).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "84035825-1cad-45e9-ad64-02dba703ccec",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading...\n",
      "From (original): https://drive.google.com/uc?id=1q87a9euo52eoFG37shPK0G5juq20x7-m\n",
      "From (redirected): https://drive.google.com/uc?id=1q87a9euo52eoFG37shPK0G5juq20x7-m&confirm=t&uuid=2ffd42de-ac62-4c27-bb8d-8e96191deeb7\n",
      "To: /tmp/tmpjboo3kuj/data.zip\n",
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 26.7M/26.7M [00:01<00:00, 26.7MB/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'/tmp/tmpjboo3kuj/data.zip'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comment if already downloaded!\n",
    "gdown.download(\"https://drive.google.com/uc?id=1q87a9euo52eoFG37shPK0G5juq20x7-m\", os.path.join(root_dir, \"data.zip\"))\n",
    "#!gdown -O data.zip \"https://drive.google.com/uc?id=1q87a9euo52eoFG37shPK0G5juq20x7-m\" --remaining-ok"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c61496ac-6720-4bcf-b1b4-4c43fe7ea095",
   "metadata": {},
   "source": [
    "We extract and reorganise the data into the relevant sub-folders:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "20092100-c8f8-4d16-9481-3117157cd586",
   "metadata": {},
   "outputs": [],
   "source": [
    "zip_obj = zipfile.ZipFile(os.path.join(root_dir, \"data.zip\"), \"r\")\n",
    "zip_obj.extractall(root_dir)\n",
    "images_t1 = root_dir / \"BRATS_SUBS2_SUBSET/T1\"\n",
    "labels = root_dir / \"BRATS_SUBS2_SUBSET/Parcellations\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd6a4ecc-b9c4-4cd4-a17b-660a975e082a",
   "metadata": {},
   "source": [
    "## Prepare dataloaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "17e92373-ada3-4d46-8121-c991968d4a2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# INPUT PARAMETERS\n",
    "input_shape = [128, 128]\n",
    "batch_size = 4\n",
    "num_workers = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7beeec55-d552-4f5c-bd56-d12475c1ca8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We create the data dictionaries that we need\n",
    "all_images = [os.path.join(images_t1, i) for i in os.listdir(images_t1)]\n",
    "np.random.shuffle(all_images)\n",
    "corresponding_labels = [\n",
    "    os.path.join(labels, i.split(\"/\")[-1].replace(i.split(\"/\")[-1].split(\"_\")[0], \"Parcellation\")) for i in all_images\n",
    "]\n",
    "input_dict = [{\"image\": i, \"label\": corresponding_labels[ind]} for ind, i in enumerate(all_images)]\n",
    "input_dict_train = input_dict[: int(len(input_dict) * 0.9)]\n",
    "input_dict_val = input_dict[int(len(input_dict) * 0.9) :]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be3237a9-082b-42fd-b3d4-cd3023edef3a",
   "metadata": {},
   "source": [
    "We define the relevant transforms:\n",
    "1. `LoadImaged`: to load the nii tumour images and labels\n",
    "2. `EnsureChannelFirstd`: to make sure that a channel dimension is added on the tensors\n",
    "3. `CenterSpatialCropd`: we ensure that the image and labels have the same shape\n",
    "4. `RandSpatialCropd`: because we are extracting 2D slices from 3D volumes, we crop slices\n",
    "5. `Resized`: to bring the shape of all images to the same value\n",
    "6. `SqueezeDimd`: we remove the 1-dimension of the slice\n",
    "7. `NormalizeIntensityd`: normalise the images\n",
    "8. `RandBiasFieldd`: bias field augmentation\n",
    "9. `RandAdjustContrastd`: random contrast augmentation\n",
    "10. `RandGaussianNoised`: random noise tradition\n",
    "11. `RandAffined`: random affine augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8b7e7a35-b6b1-4071-b450-443aeb0637f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "preliminar_shape = input_shape + [50]  # We take random slices fron the center of the brain\n",
    "crop_shape = input_shape + [1]\n",
    "base_transforms = [\n",
    "    monai.transforms.LoadImaged(keys=[\"label\", \"image\"]),\n",
    "    monai.transforms.EnsureChannelFirstd(keys=[\"image\", \"label\"]),\n",
    "    monai.transforms.CenterSpatialCropd(keys=[\"label\", \"image\"], roi_size=preliminar_shape),\n",
    "    monai.transforms.RandSpatialCropd(keys=[\"label\", \"image\"], roi_size=crop_shape, max_roi_size=crop_shape),\n",
    "    monai.transforms.SqueezeDimd(keys=[\"label\", \"image\"], dim=-1),\n",
    "    monai.transforms.Resized(keys=[\"image\", \"label\"], spatial_size=input_shape),\n",
    "]\n",
    "last_transforms = [\n",
    "    monai.transforms.CopyItemsd(keys=[\"label\"], names=[\"label_channel\"]),\n",
    "    monai.transforms.Lambdad(keys=[\"label_channel\"], func=lambda val: val != 0),\n",
    "    monai.transforms.MaskIntensityd(keys=[\"image\"], mask_key=\"label_channel\"),\n",
    "    monai.transforms.NormalizeIntensityd(keys=[\"image\"]),\n",
    "    monai.transforms.ToTensord(keys=[\"image\", \"label\"]),\n",
    "]\n",
    "\n",
    "aug_transforms = [\n",
    "    monai.transforms.RandBiasFieldd(coeff_range=(0, 0.005), prob=0.33, keys=[\"image\"]),\n",
    "    monai.transforms.RandAdjustContrastd(gamma=(0.9, 1.15), prob=0.33, keys=[\"image\"]),\n",
    "    monai.transforms.RandGaussianNoised(prob=0.33, mean=0.0, std=np.random.uniform(0.005, 0.015), keys=[\"image\"]),\n",
    "    monai.transforms.RandAffined(\n",
    "        rotate_range=[-0.05, 0.05],\n",
    "        shear_range=[0.001, 0.05],\n",
    "        scale_range=[0, 0.05],\n",
    "        padding_mode=\"zeros\",\n",
    "        mode=\"nearest\",\n",
    "        prob=0.33,\n",
    "        keys=[\"label\", \"image\"],\n",
    "    ),\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e08dd4d7-2b73-44f0-896c-66f1de7813b6",
   "metadata": {},
   "source": [
    "We create the dataset and dataloader:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "35c16de6-bf1b-490f-8768-a7041b6b1bd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_transforms = monai.transforms.Compose(base_transforms + aug_transforms + last_transforms)\n",
    "val_transforms = monai.transforms.Compose(base_transforms + last_transforms)\n",
    "\n",
    "train_dataset = monai.data.dataset.Dataset(input_dict_train, train_transforms)\n",
    "val_dataset = monai.data.dataset.Dataset(input_dict_val, val_transforms)\n",
    "train_loader = DataLoader(train_dataset, shuffle=True, batch_size=batch_size, num_workers=num_workers)\n",
    "val_loader = DataLoader(val_dataset, shuffle=False, drop_last=False, batch_size=batch_size, num_workers=num_workers)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69cdd8ec-6ea9-4e01-b488-a869909b3af7",
   "metadata": {},
   "source": [
    "We do a sanity check and plot an element of the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bf3c104c-3099-4d85-86f2-7563bf9f20d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([4, 1, 128, 128])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sanity check\n",
    "batch = next(iter(train_loader))\n",
    "print(batch[\"image\"].shape)\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.imshow(batch[\"image\"][0, 0, ...], cmap=\"gist_gray\")\n",
    "plt.axis(\"off\")\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.imshow(batch[\"label\"][0, 0, ...], cmap=\"jet\")\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc34b150-bfad-4f23-9b00-409bebcf3fbc",
   "metadata": {},
   "source": [
    "### Networks creation and losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3e4fb2d3-c653-40a2-a4fa-2439ddf7f942",
   "metadata": {},
   "outputs": [],
   "source": [
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2b67ff02-1d12-4256-ac79-7a4cd99bfe26",
   "metadata": {},
   "outputs": [],
   "source": [
    "def one_hot(input_label, label_nc):\n",
    "    # One hot encoding function for the labels\n",
    "    shape_ = list(input_label.shape)\n",
    "    shape_[1] = label_nc\n",
    "    label_out = torch.zeros(shape_)\n",
    "    for channel in range(label_nc):\n",
    "        label_out[:, channel, ...] = input_label[:, 0, ...] == channel\n",
    "    return label_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1033ac6e-b53e-4c13-9e7e-c2c88e5af220",
   "metadata": {},
   "outputs": [],
   "source": [
    "def picture_results(input_label, input_image, output_image):\n",
    "    plt.figure(figsize=(4, 1.5))\n",
    "    plt.subplot(1, 3, 1)\n",
    "    plt.imshow(torch.argmax(input_label, 1)[0, ...].detach().cpu(), cmap=\"jet\")\n",
    "    plt.axis(\"off\")\n",
    "    plt.title(\"Label\")\n",
    "    plt.subplot(1, 3, 2)\n",
    "    plt.imshow(input_image[0, 0, ...].detach().cpu(), cmap=\"gist_gray\")\n",
    "    plt.axis(\"off\")\n",
    "    plt.title(\"Input image\")\n",
    "    plt.subplot(1, 3, 3)\n",
    "    plt.imshow(output_image[0, 0, ...].detach().cpu(), cmap=\"gist_gray\")\n",
    "    plt.axis(\"off\")\n",
    "    plt.title(\"Output image\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3419f8dd-f26c-4852-bc7d-2047ca3f6826",
   "metadata": {},
   "source": [
    "SPADE Diffusion Models require two components:\n",
    "- Autoencoder, incorporating SPADE normalisation in the decoder blocks\n",
    "- Diffusion model, operating in the latent space, and incorporating SPADE normalisation in the decoding branch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3962bd41-9c31-46c5-880b-f451e8576d2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "autoencoder = SPADEAutoencoderKL(\n",
    "    spatial_dims=2,\n",
    "    in_channels=1,\n",
    "    out_channels=1,\n",
    "    num_res_blocks=(2, 2, 2, 2),\n",
    "    channels=(8, 16, 32, 64),\n",
    "    attention_levels=[False, False, False, False],\n",
    "    latent_channels=8,\n",
    "    norm_num_groups=8,\n",
    "    label_nc=6,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "78ed236e-a302-40d7-b0f5-cd39810b8319",
   "metadata": {},
   "outputs": [],
   "source": [
    "diffusion = SPADEDiffusionModelUNet(\n",
    "    spatial_dims=2,\n",
    "    in_channels=8,\n",
    "    out_channels=8,\n",
    "    num_res_blocks=(2, 2, 2, 2),\n",
    "    channels=(16, 32, 64, 128),\n",
    "    attention_levels=(False, False, True, True),\n",
    "    norm_num_groups=16,\n",
    "    with_conditioning=False,\n",
    "    label_nc=6,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d83908eb-df5b-45dc-a24d-8148f1d7ce19",
   "metadata": {},
   "source": [
    "To train the autoencoder, we are using **a Patch-GAN-based adversarial loss**, a **perceptual loss** and a basic **L1 loss** between input and output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8823799d-4762-4884-bce8-ec1faa457d07",
   "metadata": {},
   "outputs": [],
   "source": [
    "perceptual_loss = PerceptualLoss(spatial_dims=2, network_type=\"alex\")\n",
    "perceptual_loss.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6bfc5773-0a2b-441e-b1b6-911656b7342d",
   "metadata": {},
   "outputs": [],
   "source": [
    "discriminator = PatchDiscriminator(\n",
    "    spatial_dims=2,\n",
    "    num_layers_d=3,\n",
    "    channels=16,\n",
    "    in_channels=1,\n",
    "    out_channels=1,\n",
    ")\n",
    "discriminator = discriminator.to(device)\n",
    "\n",
    "adv_loss = PatchAdversarialLoss(criterion=\"least_squares\")\n",
    "adv_weight = 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "17f4bf30-09d2-4107-b0b0-3109e611a89f",
   "metadata": {},
   "outputs": [],
   "source": [
    "recon = torch.nn.L1Loss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8dc58031-feb2-48ce-94a7-6ac21b134d41",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer_g = torch.optim.Adam(autoencoder.parameters(), lr=0.0002)\n",
    "optimizer_d = torch.optim.Adam(discriminator.parameters(), lr=0.0004)\n",
    "# For mixed precision training\n",
    "scaler_g = torch.amp.GradScaler(\"cuda\")\n",
    "scaler_d = torch.amp.GradScaler(\"cuda\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2511adc2-593e-4c20-9a56-e366de82047c",
   "metadata": {},
   "source": [
    "## Training the AutoencoderKL"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0033a3c-5aee-46f3-ac50-301546ab2783",
   "metadata": {},
   "source": [
    "We used the exact same approach as the one from the 2d_ldm_tutorial to train the autoencoder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "aeac24bf-8263-4459-9d59-c79cfbdc27eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loss weights and number of epochs\n",
    "kl_weight = 1e-6\n",
    "max_epochs = 100\n",
    "val_interval = 20\n",
    "adv_weights = 0.01\n",
    "autoencoder_warm_up_n_epochs = 10\n",
    "perceptual_weight = 0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d8b575ad-0993-4a90-8204-28d7354658fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: reconstruction: 0.039634; generator: 0.000000; discriminator: 0.000000; perceptual: 0.007687; kld: 815.478556\n",
      "Train: reconstruction: 0.039850; generator: 0.000000; discriminator: 0.000000; perceptual: 0.006226; kld: 771.141073\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 20 val loss: 0.0368\n",
      "Train: reconstruction: 0.041205; generator: 0.272908; discriminator: 0.241269; perceptual: 0.006241; kld: 809.402791\n",
      "Train: reconstruction: 0.037956; generator: 0.265336; discriminator: 0.235045; perceptual: 0.009201; kld: 799.378520\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 40 val loss: 0.0349\n",
      "Train: reconstruction: 0.034912; generator: 0.319132; discriminator: 0.233328; perceptual: 0.004364; kld: 710.857808\n",
      "Train: reconstruction: 0.037357; generator: 0.336117; discriminator: 0.262539; perceptual: 0.006409; kld: 724.309652\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 60 val loss: 0.0381\n",
      "Train: reconstruction: 0.038877; generator: 0.258824; discriminator: 0.250337; perceptual: 0.007957; kld: 846.944326\n",
      "Train: reconstruction: 0.034942; generator: 0.377081; discriminator: 0.269728; perceptual: 0.005779; kld: 795.518361\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 80 val loss: 0.0328\n",
      "Train: reconstruction: 0.034282; generator: 0.324377; discriminator: 0.264228; perceptual: 0.005844; kld: 812.505910\n",
      "Train: reconstruction: 0.035039; generator: 0.410919; discriminator: 0.214465; perceptual: 0.009735; kld: 733.619568\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 100 val loss: 0.0348\n"
     ]
    }
   ],
   "source": [
    "autoencoder.to(device)\n",
    "\n",
    "# Loss storage\n",
    "epoch_recon_losses = []\n",
    "epoch_perc_losses = []\n",
    "epoch_kld_losses = []\n",
    "epoch_gen_losses = []\n",
    "epoch_disc_losses = []\n",
    "val_recon_losses = []\n",
    "\n",
    "for epoch in range(max_epochs):\n",
    "    autoencoder.train()\n",
    "    discriminator.train()\n",
    "    epoch_loss = 0\n",
    "    gen_epoch_loss = 0\n",
    "    disc_epoch_loss = 0\n",
    "    perceptual_epoch_loss = 0\n",
    "    kld_epoch_loss = 0\n",
    "    for batch in train_loader:\n",
    "        images = batch[\"image\"].to(device)\n",
    "        labels = one_hot(batch[\"label\"], 6).to(device)\n",
    "        optimizer_g.zero_grad(set_to_none=True)\n",
    "\n",
    "        with autocast(device_type=\"cuda\", enabled=True):\n",
    "            reconstruction, z_mu, z_sigma = autoencoder(images, labels)\n",
    "            recons_loss = recon(reconstruction.float(), images.float())\n",
    "            p_loss = perceptual_loss(reconstruction.float(), images.float())\n",
    "            kl_loss = 0.5 * torch.sum(z_mu.pow(2) + z_sigma.pow(2) - torch.log(z_sigma.pow(2)) - 1, dim=[1, 2, 3])\n",
    "            kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]\n",
    "            loss_g = recons_loss + (kl_weight * kl_loss) + (perceptual_weight * p_loss)\n",
    "\n",
    "            if epoch > autoencoder_warm_up_n_epochs:\n",
    "                logits_fake = discriminator(reconstruction.contiguous().float())[-1]\n",
    "                generator_loss = adv_loss(logits_fake, target_is_real=True, for_discriminator=False)\n",
    "                loss_g += adv_weight * generator_loss\n",
    "\n",
    "        scaler_g.scale(loss_g).backward()\n",
    "        scaler_g.step(optimizer_g)\n",
    "        scaler_g.update()\n",
    "\n",
    "        if epoch > autoencoder_warm_up_n_epochs:\n",
    "            with autocast(device_type=\"cuda\", enabled=True):\n",
    "                optimizer_d.zero_grad(set_to_none=True)\n",
    "                logits_fake = discriminator(reconstruction.contiguous().detach())[-1]\n",
    "                loss_d_fake = adv_loss(logits_fake, target_is_real=False, for_discriminator=True)\n",
    "                logits_real = discriminator(images.contiguous().detach())[-1]\n",
    "                loss_d_real = adv_loss(logits_real, target_is_real=True, for_discriminator=True)\n",
    "                discriminator_loss = (loss_d_fake + loss_d_real) * 0.5\n",
    "\n",
    "                loss_d = adv_weight * discriminator_loss\n",
    "\n",
    "            scaler_d.scale(loss_d).backward()\n",
    "            scaler_d.step(optimizer_d)\n",
    "            scaler_d.update()\n",
    "\n",
    "        epoch_loss += recons_loss.item()\n",
    "        perceptual_epoch_loss += p_loss.item()\n",
    "        kld_epoch_loss += kl_loss.item()\n",
    "        if epoch > autoencoder_warm_up_n_epochs:\n",
    "            gen_epoch_loss += generator_loss.item()\n",
    "            disc_epoch_loss += discriminator_loss.item()\n",
    "\n",
    "    num_steps = len(train_loader) + 1\n",
    "    epoch_recon_losses.append(epoch_loss / num_steps)\n",
    "    epoch_gen_losses.append(gen_epoch_loss / num_steps)\n",
    "    epoch_disc_losses.append(disc_epoch_loss / num_steps)\n",
    "    epoch_perc_losses.append(p_loss / num_steps)\n",
    "    epoch_kld_losses.append(kld_epoch_loss / num_steps)\n",
    "\n",
    "    if epoch % 10 == 0:\n",
    "        msgs = [\n",
    "            f\"Train: reconstruction: {epoch_recon_losses[-1]:4f}\",\n",
    "            f\" generator: {epoch_gen_losses[-1]:4f}\",\n",
    "            f\" discriminator: {epoch_disc_losses[-1]:4f}\",\n",
    "            f\" perceptual: {epoch_perc_losses[-1]:4f}\",\n",
    "            f\" kld: {epoch_kld_losses[-1]:4f}\",\n",
    "        ]\n",
    "        print(\";\".join(msgs))\n",
    "\n",
    "    if (epoch + 1) % val_interval == 0:\n",
    "        autoencoder.eval()\n",
    "        val_loss = 0\n",
    "        with torch.no_grad():\n",
    "            for val_step, batch in enumerate(val_loader, start=0):\n",
    "                images = batch[\"image\"].to(device)\n",
    "                labels = one_hot(batch[\"label\"], 6).to(device)\n",
    "                with autocast(device_type=\"cuda\", enabled=True):\n",
    "                    reconstruction, z_mu, z_sigma = autoencoder(images, labels)\n",
    "                    recons_loss = recon(images.float(), reconstruction.float())\n",
    "                val_loss += recons_loss.item()\n",
    "                # We retrieve the image to plot\n",
    "                if val_step == 0:\n",
    "                    reconstruction = reconstruction.detach().cpu()\n",
    "                    plt.figure(figsize=(5, 3))\n",
    "                    plt.subplot(1, 3, 1)\n",
    "                    plt.imshow(images[0, 0, ...].detach().cpu(), cmap=\"gist_gray\")\n",
    "                    plt.axis(\"off\")\n",
    "                    plt.subplot(1, 3, 2)\n",
    "                    plt.imshow(reconstruction[0, 0, ...], cmap=\"gist_gray\")\n",
    "                    plt.axis(\"off\")\n",
    "                    plt.subplot(1, 3, 3)\n",
    "                    plt.imshow(batch[\"label\"][0, 0, ...].detach().cpu(), cmap=\"jet\")\n",
    "                    plt.axis(\"off\")\n",
    "                    plt.show()\n",
    "\n",
    "        val_loss /= max(val_step, 1)\n",
    "        val_recon_losses.append(val_loss)\n",
    "        print(f\"epoch {epoch + 1} val loss: {val_loss:.4f}\")\n",
    "\n",
    "del discriminator\n",
    "del perceptual_loss\n",
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7be99a77-3609-4347-9577-86047a8dabf0",
   "metadata": {},
   "source": [
    "## Training the diffusion model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4ae7d82-96bd-43ba-9105-884ce9688f40",
   "metadata": {},
   "source": [
    "Likewise, we use the same approach as in the 2d_ldm_tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2d19a3c6-7fae-408e-bbff-705ab878bc20",
   "metadata": {},
   "outputs": [],
   "source": [
    "scheduler = DDPMScheduler(num_train_timesteps=1000, schedule=\"linear_beta\", beta_start=0.0015, beta_end=0.0195)\n",
    "optimizer = torch.optim.Adam(diffusion.parameters(), lr=1e-4)\n",
    "inferer = LatentDiffusionInferer(scheduler, scale_factor=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f47e6126-05d9-41b7-9c00-5d083f60c70b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0 / 200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training loss: 1.0034\n",
      "Epoch 10 / 200\n",
      "training loss: 0.9315\n",
      "Epoch 20 / 200\n",
      "training loss: 0.8709\n",
      "Epoch 30 / 200\n",
      "training loss: 0.8102\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 39 val loss: 0.7840\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:23<00:00, 42.05it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 40 / 200\n",
      "training loss: 0.7813\n",
      "Epoch 50 / 200\n",
      "training loss: 0.7013\n",
      "Epoch 60 / 200\n",
      "training loss: 0.6907\n",
      "Epoch 70 / 200\n",
      "training loss: 0.6525\n",
      "Epoch 79 val loss: 0.5309\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:25<00:00, 39.09it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 80 / 200\n",
      "training loss: 0.6065\n",
      "Epoch 90 / 200\n",
      "training loss: 0.4917\n",
      "Epoch 100 / 200\n",
      "training loss: 0.4911\n",
      "Epoch 110 / 200\n",
      "training loss: 0.5587\n",
      "Epoch 119 val loss: 0.4845\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:24<00:00, 40.34it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 120 / 200\n",
      "training loss: 0.4899\n",
      "Epoch 130 / 200\n",
      "training loss: 0.4610\n",
      "Epoch 140 / 200\n",
      "training loss: 0.4903\n",
      "Epoch 150 / 200\n",
      "training loss: 0.3912\n",
      "Epoch 159 val loss: 0.4990\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:25<00:00, 39.23it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 160 / 200\n",
      "training loss: 0.4311\n",
      "Epoch 170 / 200\n",
      "training loss: 0.4064\n",
      "Epoch 180 / 200\n",
      "training loss: 0.3768\n",
      "Epoch 190 / 200\n",
      "training loss: 0.3853\n",
      "Epoch 199 val loss: 0.2768\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:25<00:00, 39.72it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diffusion = diffusion.to(device)\n",
    "max_epochs = 200\n",
    "val_interval = 40\n",
    "epoch_losses = []\n",
    "val_losses = []\n",
    "scaler = GradScaler(\"cuda\")\n",
    "\n",
    "for epoch in range(max_epochs):\n",
    "    if epoch % 10 == 0:\n",
    "        print(\"Epoch %d / %d\" % (epoch, max_epochs))\n",
    "    diffusion.train()\n",
    "    autoencoder.eval()\n",
    "    epoch_loss = 0\n",
    "    for batch in train_loader:\n",
    "        images = batch[\"image\"].to(device)\n",
    "        labels = one_hot(batch[\"label\"], 6).to(device)\n",
    "        optimizer.zero_grad(set_to_none=True)\n",
    "        with autocast(device_type=\"cuda\", enabled=True):\n",
    "            z_mu, z_sigma = autoencoder.encode(images)\n",
    "            z = autoencoder.sampling(z_mu, z_sigma)\n",
    "            noise = torch.randn_like(z).to(device)\n",
    "            timesteps = torch.randint(0, inferer.scheduler.num_train_timesteps, (z.shape[0],), device=z.device).long()\n",
    "            noise_pred = inferer(\n",
    "                inputs=images,\n",
    "                diffusion_model=diffusion,\n",
    "                noise=noise,\n",
    "                timesteps=timesteps,\n",
    "                autoencoder_model=autoencoder,\n",
    "                seg=labels,\n",
    "            )\n",
    "            loss = F.mse_loss(noise_pred.float(), noise.float())\n",
    "\n",
    "        scaler.scale(loss).backward()\n",
    "        scaler.step(optimizer)\n",
    "        scaler.update()\n",
    "        epoch_loss += loss.item()\n",
    "\n",
    "    epoch_losses.append(epoch_loss / (len(train_loader) + 1))\n",
    "    if epoch % 10 == 0:\n",
    "        print(\"training loss: %.4f\" % (epoch_losses[-1]))\n",
    "\n",
    "    if (epoch + 1) % val_interval == 0:\n",
    "        diffusion.eval()\n",
    "        val_loss = 0\n",
    "        with torch.no_grad():\n",
    "            for batch in val_loader:\n",
    "                images = batch[\"image\"].to(device)\n",
    "                labels = one_hot(batch[\"label\"], 6).to(device)\n",
    "                with autocast(device_type=\"cuda\", enabled=True):\n",
    "                    z_mu, z_sigma = autoencoder.encode(images)\n",
    "\n",
    "                    z = autoencoder.sampling(z_mu, z_sigma)\n",
    "                    noise = torch.randn_like(z).to(device)\n",
    "                    timesteps = torch.randint(\n",
    "                        0, inferer.scheduler.num_train_timesteps, (z.shape[0],), device=z.device\n",
    "                    ).long()\n",
    "                    noise_pred = inferer(\n",
    "                        inputs=images,\n",
    "                        diffusion_model=diffusion,\n",
    "                        noise=noise,\n",
    "                        timesteps=timesteps,\n",
    "                        autoencoder_model=autoencoder,\n",
    "                        seg=labels,\n",
    "                    )\n",
    "\n",
    "                    loss = F.mse_loss(noise_pred.float(), noise.float())\n",
    "\n",
    "                val_loss += loss.item()\n",
    "        val_loss /= len(val_loader) + 1\n",
    "        val_losses.append(val_loss)\n",
    "        print(f\"Epoch {epoch} val loss: {val_loss:.4f}\")\n",
    "\n",
    "        # Sampling image during training. We use the last segmentation of our loader\n",
    "        z = torch.randn((labels.shape[0], 8, 16, 16))\n",
    "        z = z.to(device)\n",
    "        scheduler.set_timesteps(num_inference_steps=1000)\n",
    "        with autocast(device_type=\"cuda\", enabled=True):\n",
    "            decoded = inferer.sample(\n",
    "                input_noise=z, diffusion_model=diffusion, scheduler=scheduler, autoencoder_model=autoencoder, seg=labels\n",
    "            )\n",
    "        plt.figure(figsize=(5, 3))\n",
    "        plt.subplot(1, 3, 1)\n",
    "        plt.imshow(images[0, 0, ...].detach().cpu(), cmap=\"gist_gray\")\n",
    "        plt.axis(\"off\")\n",
    "        plt.subplot(1, 3, 2)\n",
    "        plt.imshow(decoded[0, 0, ...].detach().cpu(), cmap=\"gist_gray\")\n",
    "        plt.axis(\"off\")\n",
    "        plt.subplot(1, 3, 3)\n",
    "        plt.imshow(batch[\"label\"][0, 0, ...].detach().cpu(), cmap=\"jet\")\n",
    "        plt.axis(\"off\")\n",
    "        plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
